Overview

Dataset statistics

Number of variables24
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.1%
Total size in memory8.6 MiB
Average record size in memory301.7 B

Variable types

NUM21
CAT2
BOOL1

Reproduction

Analysis started2021-10-17 23:31:27.629953
Analysis finished2021-10-17 23:33:19.427602
Duration1 minute and 51.8 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 35 (0.1%) duplicate rows Duplicates
X13_BILL_AMT2 is highly correlated with X12_BILL_AMT1 and 1 other fieldsHigh correlation
X12_BILL_AMT1 is highly correlated with X13_BILL_AMT2High correlation
X14_BILL_AMT3 is highly correlated with X13_BILL_AMT2 and 1 other fieldsHigh correlation
X15_BILL_AMT4 is highly correlated with X14_BILL_AMT3 and 2 other fieldsHigh correlation
X16_BILL_AMT5 is highly correlated with X15_BILL_AMT4 and 1 other fieldsHigh correlation
X17_BILL_AMT6 is highly correlated with X15_BILL_AMT4 and 1 other fieldsHigh correlation
X19_PAY_AMT2 is highly skewed (γ1 = 30.45381745) Skewed
X6_PAY_0 has 14737 (49.1%) zeros Zeros
X7_PAY_2 has 15730 (52.4%) zeros Zeros
X8_PAY_3 has 15764 (52.5%) zeros Zeros
X9_PAY_4 has 16455 (54.9%) zeros Zeros
X10_PAY_5 has 16947 (56.5%) zeros Zeros
X11_PAY_6 has 16286 (54.3%) zeros Zeros
X12_BILL_AMT1 has 2008 (6.7%) zeros Zeros
X13_BILL_AMT2 has 2506 (8.4%) zeros Zeros
X14_BILL_AMT3 has 2870 (9.6%) zeros Zeros
X15_BILL_AMT4 has 3195 (10.7%) zeros Zeros
X16_BILL_AMT5 has 3506 (11.7%) zeros Zeros
X17_BILL_AMT6 has 4020 (13.4%) zeros Zeros
X18_PAY_AMT1 has 5249 (17.5%) zeros Zeros
X19_PAY_AMT2 has 5396 (18.0%) zeros Zeros
X20_PAY_AMT3 has 5968 (19.9%) zeros Zeros
X21_PAY_AMT4 has 6408 (21.4%) zeros Zeros
X22_PAY_AMT5 has 6703 (22.3%) zeros Zeros
X23_PAY_AMT6 has 7173 (23.9%) zeros Zeros

Variables

X1_LIMIT_BAL
Real number (ℝ≥0)

Distinct count81
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.32266666667
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Memory size234.5 KiB
2021-10-18T01:33:19.557533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.6616
Coefficient of variation (CV)0.7746854124
Kurtosis0.5362628964
Mean167484.3227
Median Absolute Deviation (MAD)90000
Skewness0.9928669605
Sum5024529680
Variance1.683445568e+10
2021-10-18T01:33:19.703445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50000336511.2%
 
2000019766.6%
 
3000016105.4%
 
8000015675.2%
 
20000015285.1%
 
15000011103.7%
 
10000010483.5%
 
1800009953.3%
 
3600008812.9%
 
600008252.8%
 
1400007492.5%
 
2300007372.5%
 
700007312.4%
 
2100007302.4%
 
1300007292.4%
 
1200007262.4%
 
5000007222.4%
 
1600006942.3%
 
900006512.2%
 
2400006192.1%
 
1100005882.0%
 
3000005541.8%
 
1700005321.8%
 
2600005211.7%
 
2800004931.6%
 
Other values (56)531917.7%
 
ValueCountFrequency (%) 
100004931.6%
 
160002< 0.1%
 
2000019766.6%
 
3000016105.4%
 
400002300.8%
 
50000336511.2%
 
600008252.8%
 
700007312.4%
 
8000015675.2%
 
900006512.2%
 
ValueCountFrequency (%) 
10000001< 0.1%
 
8000002< 0.1%
 
7800002< 0.1%
 
7600001< 0.1%
 
7500004< 0.1%
 
7400002< 0.1%
 
7300002< 0.1%
 
7200003< 0.1%
 
7100006< 0.1%
 
7000008< 0.1%
 

X2_GENDER
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
female
18112
male
11888
ValueCountFrequency (%) 
female1811260.4%
 
male1188839.6%
 
2021-10-18T01:33:19.913324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.207466667
Min length4

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e4811230.8%
 
m3000019.2%
 
a3000019.2%
 
l3000019.2%
 
f1811211.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter156224100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e4811230.8%
 
m3000019.2%
 
a3000019.2%
 
l3000019.2%
 
f1811211.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin156224100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e4811230.8%
 
m3000019.2%
 
a3000019.2%
 
l3000019.2%
 
f1811211.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII156224100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e4811230.8%
 
m3000019.2%
 
a3000019.2%
 
l3000019.2%
 
f1811211.6%
 

X3_EDUCATION
Real number (ℝ≥0)

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8531333333333333
Minimum0
Maximum6
Zeros14
Zeros (%)< 0.1%
Memory size234.5 KiB
2021-10-18T01:33:20.093242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7903486597
Coefficient of variation (CV)0.426493143
Kurtosis2.078621603
Mean1.853133333
Median Absolute Deviation (MAD)1
Skewness0.9709720486
Sum55594
Variance0.6246510039
2021-10-18T01:33:20.246133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
21403046.8%
 
11058535.3%
 
3491716.4%
 
52800.9%
 
41230.4%
 
6510.2%
 
014< 0.1%
 
ValueCountFrequency (%) 
014< 0.1%
 
11058535.3%
 
21403046.8%
 
3491716.4%
 
41230.4%
 
52800.9%
 
6510.2%
 
ValueCountFrequency (%) 
6510.2%
 
52800.9%
 
41230.4%
 
3491716.4%
 
21403046.8%
 
11058535.3%
 
014< 0.1%
 
Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
single
15964
married
13659
others
 
377
ValueCountFrequency (%) 
single1596453.2%
 
married1365945.5%
 
others3771.3%
 
2021-10-18T01:33:20.446018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.4553
Min length6

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e3000015.5%
 
i2962315.3%
 
r2769514.3%
 
s163418.4%
 
n159648.2%
 
g159648.2%
 
l159648.2%
 
m136597.1%
 
a136597.1%
 
d136597.1%
 
o3770.2%
 
t3770.2%
 
h3770.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter193659100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3000015.5%
 
i2962315.3%
 
r2769514.3%
 
s163418.4%
 
n159648.2%
 
g159648.2%
 
l159648.2%
 
m136597.1%
 
a136597.1%
 
d136597.1%
 
o3770.2%
 
t3770.2%
 
h3770.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin193659100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e3000015.5%
 
i2962315.3%
 
r2769514.3%
 
s163418.4%
 
n159648.2%
 
g159648.2%
 
l159648.2%
 
m136597.1%
 
a136597.1%
 
d136597.1%
 
o3770.2%
 
t3770.2%
 
h3770.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII193659100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e3000015.5%
 
i2962315.3%
 
r2769514.3%
 
s163418.4%
 
n159648.2%
 
g159648.2%
 
l159648.2%
 
m136597.1%
 
a136597.1%
 
d136597.1%
 
o3770.2%
 
t3770.2%
 
h3770.2%
 

X5_AGE
Real number (ℝ≥0)

Distinct count56
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Memory size234.5 KiB
2021-10-18T01:33:20.602926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217904068
Coefficient of variation (CV)0.2597653709
Kurtosis0.04430337824
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.7322458688
Sum1064565
Variance84.96975541
2021-10-18T01:33:20.761837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2916055.3%
 
2714774.9%
 
2814094.7%
 
3013954.7%
 
2612564.2%
 
3112174.1%
 
2511864.0%
 
3411623.9%
 
3211583.9%
 
3311463.8%
 
2411273.8%
 
3511133.7%
 
3611083.7%
 
3710413.5%
 
399543.2%
 
389443.1%
 
239313.1%
 
408702.9%
 
418242.7%
 
427942.6%
 
447002.3%
 
436702.2%
 
456172.1%
 
465701.9%
 
225601.9%
 
Other values (31)416613.9%
 
ValueCountFrequency (%) 
21670.2%
 
225601.9%
 
239313.1%
 
2411273.8%
 
2511864.0%
 
2612564.2%
 
2714774.9%
 
2814094.7%
 
2916055.3%
 
3013954.7%
 
ValueCountFrequency (%) 
791< 0.1%
 
753< 0.1%
 
741< 0.1%
 
734< 0.1%
 
723< 0.1%
 
713< 0.1%
 
7010< 0.1%
 
69150.1%
 
685< 0.1%
 
67160.1%
 

X6_PAY_0
Real number (ℝ)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Memory size234.5 KiB
2021-10-18T01:33:20.926743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.123801528
Coefficient of variation (CV)-67.29350467
Kurtosis2.720715042
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.7319749269
Sum-501
Variance1.262929874
2021-10-18T01:33:21.055666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01473749.1%
 
-1568619.0%
 
1368812.3%
 
-227599.2%
 
226678.9%
 
33221.1%
 
4760.3%
 
5260.1%
 
8190.1%
 
611< 0.1%
 
79< 0.1%
 
ValueCountFrequency (%) 
-227599.2%
 
-1568619.0%
 
01473749.1%
 
1368812.3%
 
226678.9%
 
33221.1%
 
4760.3%
 
5260.1%
 
611< 0.1%
 
79< 0.1%
 
ValueCountFrequency (%) 
8190.1%
 
79< 0.1%
 
611< 0.1%
 
5260.1%
 
4760.3%
 
33221.1%
 
226678.9%
 
1368812.3%
 
01473749.1%
 
-1568619.0%
 

X7_PAY_2
Real number (ℝ)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13376666666666667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Memory size234.5 KiB
2021-10-18T01:33:21.210577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197185973
Coefficient of variation (CV)-8.949807922
Kurtosis1.57041773
Mean-0.1337666667
Median Absolute Deviation (MAD)0
Skewness0.7905650222
Sum-4013
Variance1.433254254
2021-10-18T01:33:21.340524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01573052.4%
 
-1605020.2%
 
2392713.1%
 
-2378212.6%
 
33261.1%
 
4990.3%
 
1280.1%
 
5250.1%
 
7200.1%
 
612< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
-2378212.6%
 
-1605020.2%
 
01573052.4%
 
1280.1%
 
2392713.1%
 
33261.1%
 
4990.3%
 
5250.1%
 
612< 0.1%
 
7200.1%
 
ValueCountFrequency (%) 
81< 0.1%
 
7200.1%
 
612< 0.1%
 
5250.1%
 
4990.3%
 
33261.1%
 
2392713.1%
 
1280.1%
 
01573052.4%
 
-1605020.2%
 

X8_PAY_3
Real number (ℝ)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Memory size234.5 KiB
2021-10-18T01:33:21.488418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.196867568
Coefficient of variation (CV)-7.201369245
Kurtosis2.084435875
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.8406818269
Sum-4986
Variance1.432491976
2021-10-18T01:33:21.616344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01576452.5%
 
-1593819.8%
 
-2408513.6%
 
2381912.7%
 
32400.8%
 
4760.3%
 
7270.1%
 
6230.1%
 
5210.1%
 
14< 0.1%
 
83< 0.1%
 
ValueCountFrequency (%) 
-2408513.6%
 
-1593819.8%
 
01576452.5%
 
14< 0.1%
 
2381912.7%
 
32400.8%
 
4760.3%
 
5210.1%
 
6230.1%
 
7270.1%
 
ValueCountFrequency (%) 
83< 0.1%
 
7270.1%
 
6230.1%
 
5210.1%
 
4760.3%
 
32400.8%
 
2381912.7%
 
14< 0.1%
 
01576452.5%
 
-1593819.8%
 

X9_PAY_4
Real number (ℝ)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.22066666666666668
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Memory size234.5 KiB
2021-10-18T01:33:21.768260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.169138622
Coefficient of variation (CV)-5.29821128
Kurtosis3.496983496
Mean-0.2206666667
Median Absolute Deviation (MAD)0
Skewness0.9996294133
Sum-6620
Variance1.366885118
2021-10-18T01:33:21.909179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01645554.9%
 
-1568719.0%
 
-2434814.5%
 
2315910.5%
 
31800.6%
 
4690.2%
 
7580.2%
 
5350.1%
 
65< 0.1%
 
82< 0.1%
 
12< 0.1%
 
ValueCountFrequency (%) 
-2434814.5%
 
-1568719.0%
 
01645554.9%
 
12< 0.1%
 
2315910.5%
 
31800.6%
 
4690.2%
 
5350.1%
 
65< 0.1%
 
7580.2%
 
ValueCountFrequency (%) 
82< 0.1%
 
7580.2%
 
65< 0.1%
 
5350.1%
 
4690.2%
 
31800.6%
 
2315910.5%
 
12< 0.1%
 
01645554.9%
 
-1568719.0%
 

X10_PAY_5
Real number (ℝ)

ZEROS

Distinct count10
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Memory size234.5 KiB
2021-10-18T01:33:22.067088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.133187406
Coefficient of variation (CV)-4.256902352
Kurtosis3.989748144
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197025
Sum-7986
Variance1.284113697
2021-10-18T01:33:22.207008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01694756.5%
 
-1553918.5%
 
-2454615.2%
 
226268.8%
 
31780.6%
 
4840.3%
 
7580.2%
 
5170.1%
 
64< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
-2454615.2%
 
-1553918.5%
 
01694756.5%
 
226268.8%
 
31780.6%
 
4840.3%
 
5170.1%
 
64< 0.1%
 
7580.2%
 
81< 0.1%
 
ValueCountFrequency (%) 
81< 0.1%
 
7580.2%
 
64< 0.1%
 
5170.1%
 
4840.3%
 
31780.6%
 
226268.8%
 
01694756.5%
 
-1553918.5%
 
-2454615.2%
 

X11_PAY_6
Real number (ℝ)

ZEROS

Distinct count10
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Memory size234.5 KiB
2021-10-18T01:33:22.355942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.149987626
Coefficient of variation (CV)-3.950489954
Kurtosis3.42653413
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.9480293916
Sum-8733
Variance1.322471539
2021-10-18T01:33:22.484848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01628654.3%
 
-1574019.1%
 
-2489516.3%
 
227669.2%
 
31840.6%
 
4490.2%
 
7460.2%
 
6190.1%
 
513< 0.1%
 
82< 0.1%
 
ValueCountFrequency (%) 
-2489516.3%
 
-1574019.1%
 
01628654.3%
 
227669.2%
 
31840.6%
 
4490.2%
 
513< 0.1%
 
6190.1%
 
7460.2%
 
82< 0.1%
 
ValueCountFrequency (%) 
82< 0.1%
 
7460.2%
 
6190.1%
 
513< 0.1%
 
4490.2%
 
31840.6%
 
227669.2%
 
01628654.3%
 
-1574019.1%
 
-2489516.3%
 

X12_BILL_AMT1
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count22723
Unique (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.3309
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Memory size234.5 KiB
2021-10-18T01:33:22.652771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.86058
Coefficient of variation (CV)1.437545339
Kurtosis9.806289341
Mean51223.3309
Median Absolute Deviation (MAD)21800.5
Skewness2.663861022
Sum1536699927
Variance5422239963
2021-10-18T01:33:22.787694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
020086.7%
 
3902440.8%
 
780760.3%
 
326720.2%
 
316630.2%
 
2500590.2%
 
396490.2%
 
2400390.1%
 
416290.1%
 
500250.1%
 
1050250.1%
 
1473240.1%
 
-200220.1%
 
1261220.1%
 
-5220.1%
 
836210.1%
 
264200.1%
 
-3190.1%
 
291190.1%
 
600180.1%
 
-2170.1%
 
360170.1%
 
-4160.1%
 
1000160.1%
 
-1160.1%
 
Other values (22698)2704290.1%
 
ValueCountFrequency (%) 
-1655801< 0.1%
 
-1549731< 0.1%
 
-153081< 0.1%
 
-143861< 0.1%
 
-115451< 0.1%
 
-106821< 0.1%
 
-98021< 0.1%
 
-90951< 0.1%
 
-81871< 0.1%
 
-74381< 0.1%
 
ValueCountFrequency (%) 
9645111< 0.1%
 
7468141< 0.1%
 
6530621< 0.1%
 
6304581< 0.1%
 
6266481< 0.1%
 
6217491< 0.1%
 
6138601< 0.1%
 
6107231< 0.1%
 
6085941< 0.1%
 
6040191< 0.1%
 

X13_BILL_AMT2
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count22346
Unique (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.07516666667
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Memory size234.5 KiB
2021-10-18T01:33:22.949601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.76878
Coefficient of variation (CV)1.447236829
Kurtosis10.30294592
Mean49179.07517
Median Absolute Deviation (MAD)20810
Skewness2.705220853
Sum1475372255
Variance5065705363
2021-10-18T01:33:23.090520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
025068.4%
 
3902310.8%
 
326750.2%
 
780750.2%
 
316720.2%
 
2500510.2%
 
396510.2%
 
2400420.1%
 
-200290.1%
 
416280.1%
 
1050250.1%
 
1261240.1%
 
1473230.1%
 
1000230.1%
 
264220.1%
 
291220.1%
 
-3210.1%
 
-2210.1%
 
150200.1%
 
-5190.1%
 
200180.1%
 
-1180.1%
 
-18180.1%
 
300170.1%
 
-4160.1%
 
Other values (22321)2653388.4%
 
ValueCountFrequency (%) 
-697771< 0.1%
 
-675261< 0.1%
 
-333501< 0.1%
 
-300001< 0.1%
 
-262141< 0.1%
 
-247041< 0.1%
 
-247021< 0.1%
 
-229601< 0.1%
 
-186181< 0.1%
 
-180881< 0.1%
 
ValueCountFrequency (%) 
9839311< 0.1%
 
7439701< 0.1%
 
6715631< 0.1%
 
6467701< 0.1%
 
6244751< 0.1%
 
6059431< 0.1%
 
5977931< 0.1%
 
5868251< 0.1%
 
5817751< 0.1%
 
5776811< 0.1%
 

X14_BILL_AMT3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count22026
Unique (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.1548
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Memory size234.5 KiB
2021-10-18T01:33:23.255407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.38743
Coefficient of variation (CV)1.475106015
Kurtosis19.78325514
Mean47013.1548
Median Absolute Deviation (MAD)19708.5
Skewness3.087830046
Sum1410394644
Variance4809337537
2021-10-18T01:33:23.411317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
028709.6%
 
3902750.9%
 
780740.2%
 
326630.2%
 
316620.2%
 
396480.2%
 
2500400.1%
 
2400390.1%
 
416290.1%
 
200270.1%
 
1000250.1%
 
-1240.1%
 
500230.1%
 
291230.1%
 
-200230.1%
 
264230.1%
 
-2220.1%
 
1050220.1%
 
150200.1%
 
-18190.1%
 
10000190.1%
 
-3190.1%
 
1261180.1%
 
380170.1%
 
600160.1%
 
Other values (22001)2616087.2%
 
ValueCountFrequency (%) 
-1572641< 0.1%
 
-615061< 0.1%
 
-461271< 0.1%
 
-340411< 0.1%
 
-254431< 0.1%
 
-247021< 0.1%
 
-203201< 0.1%
 
-177061< 0.1%
 
-159101< 0.1%
 
-156411< 0.1%
 
ValueCountFrequency (%) 
16640891< 0.1%
 
8550861< 0.1%
 
6931311< 0.1%
 
6896431< 0.1%
 
6896271< 0.1%
 
6320411< 0.1%
 
5974151< 0.1%
 
5789711< 0.1%
 
5779571< 0.1%
 
5770151< 0.1%
 

X15_BILL_AMT4
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count21548
Unique (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.94896666666
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Memory size234.5 KiB
2021-10-18T01:33:23.596210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.85613
Coefficient of variation (CV)1.487019671
Kurtosis11.30932483
Mean43262.94897
Median Absolute Deviation (MAD)18656
Skewness2.821965291
Sum1297888469
Variance4138716378
2021-10-18T01:33:23.735132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0319510.7%
 
3902460.8%
 
7801010.3%
 
316680.2%
 
326620.2%
 
396440.1%
 
150390.1%
 
2400390.1%
 
2500340.1%
 
1000330.1%
 
416330.1%
 
-3270.1%
 
-1250.1%
 
1261240.1%
 
1050240.1%
 
-18240.1%
 
-2230.1%
 
300220.1%
 
291210.1%
 
600200.1%
 
10000170.1%
 
632170.1%
 
264170.1%
 
1170160.1%
 
-10160.1%
 
Other values (21523)2581386.0%
 
ValueCountFrequency (%) 
-1700001< 0.1%
 
-813341< 0.1%
 
-651671< 0.1%
 
-506161< 0.1%
 
-466271< 0.1%
 
-345031< 0.1%
 
-274901< 0.1%
 
-243031< 0.1%
 
-221081< 0.1%
 
-203201< 0.1%
 
ValueCountFrequency (%) 
8915861< 0.1%
 
7068641< 0.1%
 
6286991< 0.1%
 
6168361< 0.1%
 
5728051< 0.1%
 
5690341< 0.1%
 
5656691< 0.1%
 
5635431< 0.1%
 
5480201< 0.1%
 
5426531< 0.1%
 

X16_BILL_AMT5
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count21010
Unique (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.40096666667
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Memory size234.5 KiB
2021-10-18T01:33:23.904551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.15577
Coefficient of variation (CV)1.508187617
Kurtosis12.30588129
Mean40311.40097
Median Absolute Deviation (MAD)17688.5
Skewness2.876379867
Sum1209342029
Variance3696294150
2021-10-18T01:33:24.039474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0350611.7%
 
3902350.8%
 
780940.3%
 
316790.3%
 
326620.2%
 
150580.2%
 
396470.2%
 
2400390.1%
 
2500370.1%
 
416360.1%
 
-18320.1%
 
1000300.1%
 
300250.1%
 
-1240.1%
 
1050230.1%
 
10000230.1%
 
1473230.1%
 
500210.1%
 
600210.1%
 
1261190.1%
 
291190.1%
 
540180.1%
 
-3180.1%
 
20000170.1%
 
-200170.1%
 
Other values (20985)2547784.9%
 
ValueCountFrequency (%) 
-813341< 0.1%
 
-613721< 0.1%
 
-530071< 0.1%
 
-466271< 0.1%
 
-375941< 0.1%
 
-361561< 0.1%
 
-304811< 0.1%
 
-283351< 0.1%
 
-230031< 0.1%
 
-207531< 0.1%
 
ValueCountFrequency (%) 
9271711< 0.1%
 
8235401< 0.1%
 
5870671< 0.1%
 
5517021< 0.1%
 
5478801< 0.1%
 
5306721< 0.1%
 
5243151< 0.1%
 
5161391< 0.1%
 
5141141< 0.1%
 
5082131< 0.1%
 

X17_BILL_AMT6
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count20604
Unique (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.7604
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Memory size234.5 KiB
2021-10-18T01:33:24.207377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.10754
Coefficient of variation (CV)1.53206613
Kurtosis12.27070529
Mean38871.7604
Median Absolute Deviation (MAD)16755
Skewness2.846644576
Sum1166152812
Variance3546691724
2021-10-18T01:33:24.357291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0402013.4%
 
3902070.7%
 
780860.3%
 
150780.3%
 
316770.3%
 
326560.2%
 
396450.1%
 
416360.1%
 
-18330.1%
 
2400320.1%
 
1000310.1%
 
2500300.1%
 
500290.1%
 
540290.1%
 
10000270.1%
 
300260.1%
 
-1250.1%
 
930240.1%
 
1050230.1%
 
-2230.1%
 
291220.1%
 
-200200.1%
 
1650180.1%
 
20000180.1%
 
1320170.1%
 
Other values (20579)2496883.2%
 
ValueCountFrequency (%) 
-3396031< 0.1%
 
-2090511< 0.1%
 
-1509531< 0.1%
 
-946251< 0.1%
 
-738951< 0.1%
 
-570601< 0.1%
 
-514431< 0.1%
 
-511831< 0.1%
 
-466271< 0.1%
 
-457341< 0.1%
 
ValueCountFrequency (%) 
9616641< 0.1%
 
6999441< 0.1%
 
5686381< 0.1%
 
5277111< 0.1%
 
5275661< 0.1%
 
5149751< 0.1%
 
5137981< 0.1%
 
5119051< 0.1%
 
5013701< 0.1%
 
4991001< 0.1%
 

X18_PAY_AMT1
Real number (ℝ≥0)

ZEROS

Distinct count7943
Unique (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Memory size234.5 KiB
2021-10-18T01:33:24.522196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28035
Coefficient of variation (CV)2.924524575
Kurtosis415.2547427
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.66836433
Sum169907415
Variance274342256.1
2021-10-18T01:33:24.672110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0524917.5%
 
200013634.5%
 
30008913.0%
 
50006982.3%
 
15005071.7%
 
40004261.4%
 
100004011.3%
 
10003651.2%
 
25002981.0%
 
60002941.0%
 
3902070.7%
 
70001950.7%
 
35001850.6%
 
13001840.6%
 
80001760.6%
 
16001740.6%
 
18001670.6%
 
17001570.5%
 
12001360.5%
 
21001290.4%
 
45001080.4%
 
22001080.4%
 
1400970.3%
 
15000970.3%
 
3200840.3%
 
Other values (7918)1730457.7%
 
ValueCountFrequency (%) 
0524917.5%
 
19< 0.1%
 
214< 0.1%
 
3150.1%
 
4180.1%
 
512< 0.1%
 
6150.1%
 
79< 0.1%
 
88< 0.1%
 
97< 0.1%
 
ValueCountFrequency (%) 
8735521< 0.1%
 
5050001< 0.1%
 
4933581< 0.1%
 
4239031< 0.1%
 
4050161< 0.1%
 
3681991< 0.1%
 
3230141< 0.1%
 
3048151< 0.1%
 
3020001< 0.1%
 
3000391< 0.1%
 

X19_PAY_AMT2
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count7899
Unique (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Memory size234.5 KiB
2021-10-18T01:33:24.825025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.8704
Coefficient of variation (CV)3.891274139
Kurtosis1641.631911
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.45381745
Sum177634905
Variance530881708.9
2021-10-18T01:33:24.995924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0539618.0%
 
200012904.3%
 
30008572.9%
 
50007172.4%
 
10005942.0%
 
15005211.7%
 
40004101.4%
 
100003181.1%
 
60002830.9%
 
25002510.8%
 
3902350.8%
 
12002080.7%
 
16001820.6%
 
13001730.6%
 
70001700.6%
 
35001670.6%
 
80001460.5%
 
14001430.5%
 
18001360.5%
 
17001350.4%
 
22001170.4%
 
21001090.4%
 
45001000.3%
 
20000800.3%
 
9000760.3%
 
Other values (7874)1718657.3%
 
ValueCountFrequency (%) 
0539618.0%
 
1150.1%
 
2200.1%
 
3180.1%
 
411< 0.1%
 
5250.1%
 
68< 0.1%
 
712< 0.1%
 
89< 0.1%
 
96< 0.1%
 
ValueCountFrequency (%) 
16842591< 0.1%
 
12270821< 0.1%
 
12154711< 0.1%
 
10245161< 0.1%
 
5804641< 0.1%
 
4155521< 0.1%
 
4010031< 0.1%
 
3881261< 0.1%
 
3852281< 0.1%
 
3849861< 0.1%
 

X20_PAY_AMT3
Real number (ℝ≥0)

ZEROS

Distinct count7518
Unique (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Memory size234.5 KiB
2021-10-18T01:33:25.168825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.96147
Coefficient of variation (CV)3.36931393
Kurtosis564.3112295
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.21663544
Sum156770445
Variance310005092.2
2021-10-18T01:33:25.332735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0596819.9%
 
200012854.3%
 
100011033.7%
 
30008702.9%
 
50007212.4%
 
15004901.6%
 
40003811.3%
 
100003121.0%
 
12002430.8%
 
60002410.8%
 
25002280.8%
 
3902070.7%
 
13001640.5%
 
5001570.5%
 
70001540.5%
 
80001430.5%
 
35001420.5%
 
16001350.4%
 
14001220.4%
 
18001200.4%
 
17001050.4%
 
20000860.3%
 
1100840.3%
 
4500830.3%
 
15000790.3%
 
Other values (7493)1637754.6%
 
ValueCountFrequency (%) 
0596819.9%
 
113< 0.1%
 
2190.1%
 
314< 0.1%
 
4150.1%
 
5180.1%
 
614< 0.1%
 
7180.1%
 
810< 0.1%
 
912< 0.1%
 
ValueCountFrequency (%) 
8960401< 0.1%
 
8890431< 0.1%
 
5082291< 0.1%
 
4175881< 0.1%
 
4009721< 0.1%
 
3970921< 0.1%
 
3804781< 0.1%
 
3717181< 0.1%
 
3493951< 0.1%
 
3442611< 0.1%
 

X21_PAY_AMT4
Real number (ℝ≥0)

ZEROS

Distinct count6937
Unique (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.076866666666
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Memory size234.5 KiB
2021-10-18T01:33:25.556603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.15974
Coefficient of variation (CV)3.246147995
Kurtosis277.3337677
Mean4826.076867
Median Absolute Deviation (MAD)1500
Skewness12.90498482
Sum144782306
Variance245428561.1
2021-10-18T01:33:25.718507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0640821.4%
 
100013944.6%
 
200012144.0%
 
30008873.0%
 
50008102.7%
 
15004411.5%
 
40004021.3%
 
100003411.1%
 
25002590.9%
 
5002580.9%
 
60002550.9%
 
3902080.7%
 
12001750.6%
 
35001690.6%
 
70001630.5%
 
7001410.5%
 
11001410.5%
 
6001220.4%
 
80001180.4%
 
800940.3%
 
1300870.3%
 
1600860.3%
 
20000860.3%
 
1800800.3%
 
4500770.3%
 
Other values (6912)1558451.9%
 
ValueCountFrequency (%) 
0640821.4%
 
1220.1%
 
2220.1%
 
313< 0.1%
 
4200.1%
 
512< 0.1%
 
6160.1%
 
711< 0.1%
 
87< 0.1%
 
99< 0.1%
 
ValueCountFrequency (%) 
6210001< 0.1%
 
5288971< 0.1%
 
4970001< 0.1%
 
4321301< 0.1%
 
4000461< 0.1%
 
3317881< 0.1%
 
3309821< 0.1%
 
3200081< 0.1%
 
3130941< 0.1%
 
2929621< 0.1%
 

X22_PAY_AMT5
Real number (ℝ≥0)

ZEROS

Distinct count6897
Unique (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.387633333334
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Memory size234.5 KiB
2021-10-18T01:33:25.917415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.30568
Coefficient of variation (CV)3.183386475
Kurtosis180.0639402
Mean4799.387633
Median Absolute Deviation (MAD)1500
Skewness11.12741705
Sum143981629
Variance233426624.4
2021-10-18T01:33:26.046341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0670322.3%
 
100013404.5%
 
200013234.4%
 
30009473.2%
 
50008142.7%
 
15004261.4%
 
40004011.3%
 
100003431.1%
 
5002500.8%
 
60002470.8%
 
25002330.8%
 
3901790.6%
 
12001620.5%
 
70001590.5%
 
35001580.5%
 
11001250.4%
 
80001180.4%
 
7001160.4%
 
6001150.4%
 
16001010.3%
 
20000940.3%
 
1300910.3%
 
1800890.3%
 
4500870.3%
 
800830.3%
 
Other values (6872)1529651.0%
 
ValueCountFrequency (%) 
0670322.3%
 
1210.1%
 
213< 0.1%
 
313< 0.1%
 
412< 0.1%
 
59< 0.1%
 
67< 0.1%
 
79< 0.1%
 
86< 0.1%
 
96< 0.1%
 
ValueCountFrequency (%) 
4265291< 0.1%
 
4179901< 0.1%
 
3880711< 0.1%
 
3792671< 0.1%
 
3320001< 0.1%
 
3317881< 0.1%
 
3309821< 0.1%
 
3268891< 0.1%
 
3170771< 0.1%
 
3101351< 0.1%
 

X23_PAY_AMT6
Real number (ℝ≥0)

ZEROS

Distinct count6939
Unique (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.502566666667
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Memory size234.5 KiB
2021-10-18T01:33:26.194257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.46578
Coefficient of variation (CV)3.408581541
Kurtosis167.1614296
Mean5215.502567
Median Absolute Deviation (MAD)1500
Skewness10.64072733
Sum156465077
Variance316038289.4
2021-10-18T01:33:26.323160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0717323.9%
 
100012994.3%
 
200012954.3%
 
30009143.0%
 
50008082.7%
 
15004391.5%
 
40004111.4%
 
100003561.2%
 
5002470.8%
 
60002200.7%
 
25002140.7%
 
3901770.6%
 
35001590.5%
 
70001490.5%
 
12001480.5%
 
80001150.4%
 
6001120.4%
 
11001090.4%
 
45001010.3%
 
1600990.3%
 
700980.3%
 
800960.3%
 
780910.3%
 
2200800.3%
 
1700800.3%
 
Other values (6914)1501050.0%
 
ValueCountFrequency (%) 
0717323.9%
 
1200.1%
 
29< 0.1%
 
314< 0.1%
 
412< 0.1%
 
57< 0.1%
 
66< 0.1%
 
75< 0.1%
 
86< 0.1%
 
97< 0.1%
 
ValueCountFrequency (%) 
5286661< 0.1%
 
5271431< 0.1%
 
4430011< 0.1%
 
4220001< 0.1%
 
4035001< 0.1%
 
3770001< 0.1%
 
3724951< 0.1%
 
3512821< 0.1%
 
3452931< 0.1%
 
3080001< 0.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364
1
6636
ValueCountFrequency (%) 
02336477.9%
 
1663622.1%
 

Interactions

2021-10-18T01:31:37.770133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:38.014993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:38.235889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:38.435772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:38.629663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:38.823551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:39.017440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:39.213311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:39.471158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:39.711020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:39.977868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:40.181843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:40.429232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:40.748053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:40.961930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:41.179825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:41.370692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:41.584592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:41.780459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:41.991337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:42.183249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:42.592013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:42.814864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:43.018767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:43.221651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:43.421535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:43.618402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:43.813310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:44.006200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:44.199090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:44.393979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:44.600860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:44.801744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:45.031611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:45.253466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:45.455370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:45.691214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:45.909087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:46.153947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:46.362828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:46.576726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:46.770614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:46.969501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:47.171385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:47.370269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:47.578130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:47.789009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:48.014879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:48.211787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:48.422643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:48.620531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:48.836430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:49.054303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:49.291147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:49.577986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:49.790883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:50.127687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:50.350538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:50.602415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:50.836282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:51.033170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:51.309994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:51.515892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:51.715779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:51.923656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:52.121543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:52.326406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:52.531308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:52.716201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:52.903096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:53.088987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:53.272883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:53.458777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:53.655664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:53.847552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:54.054434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:54.249300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:54.445211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:54.703040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:54.906944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:55.121822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:55.353670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:55.616522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:55.833391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:56.075294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:56.304667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:56.580487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:56.855331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:57.132170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:57.385025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:57.629893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:57.847762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:58.050646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:58.248553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:58.447438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:58.648323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:58.869195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:59.083051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:59.302946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:59.521823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:31:59.910577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:00.189417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:00.496240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:00.697146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:00.887038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:01.079928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:01.273814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:01.531645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:01.743525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:02.048349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:02.238261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:02.432151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:02.645033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:02.882869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:03.071783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:03.394578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:03.632461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:03.915281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:04.265077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:04.481973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:04.803770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:05.075616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:05.384453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:05.667274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:05.887146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:06.267929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:06.626721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:06.861587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:07.147424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:07.451249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:07.724092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:07.997936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:08.367726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:08.574627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:08.855451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:09.156293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:09.426116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:09.619028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:09.889851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:10.110726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:10.388600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:10.733367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:11.028196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:11.278058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:11.496927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:11.831736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:12.204542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:12.409746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:12.653589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:13.115321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:13.342192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:13.548074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:13.862893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-18T01:32:58.538342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:58.758198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:58.983086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:59.196966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:59.440807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:59.688659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:32:59.927522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:00.125438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:00.351337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:00.585203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:00.840057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:01.035941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:01.218859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:01.414747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:01.603639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:01.809521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:02.006407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:02.186305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:02.386189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:02.567063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:02.770970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:02.951864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:03.154749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:03.335645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:03.522538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:03.725421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:03.937299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:04.142181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:04.339068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:04.535956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:04.731842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:04.932705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:05.136589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:05.340495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:05.551373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:05.756255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:05.975249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:06.191128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:06.402008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:06.634895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:06.834760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:07.065624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:07.259513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:07.465395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:07.660282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:07.864190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:08.058054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:08.250947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:08.452830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:08.648718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:08.842606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:09.396288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:09.593177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:09.788063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:09.984952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:10.185833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:10.372726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:10.572611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:10.758527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:10.952416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:11.166273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:11.359163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:11.574039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:11.766929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:11.998796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:12.273635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:12.487512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:12.767352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:12.983230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:13.233087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:13.439969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:13.701818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:13.952675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:14.216524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:14.422405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:14.694270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:14.897133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:15.161982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:15.412838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:15.722661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:15.977509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:16.266415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:16.492286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:16.779121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:17.056985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:17.269843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:17.471727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-18T01:33:26.552032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-18T01:33:27.095739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-18T01:33:27.599428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-18T01:33:28.150114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-10-18T01:33:28.617846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-10-18T01:33:17.948471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-18T01:33:19.012841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

X1_LIMIT_BALX2_GENDERX3_EDUCATIONX4_MARITAL_STATUSX5_AGEX6_PAY_0X7_PAY_2X8_PAY_3X9_PAY_4X10_PAY_5X11_PAY_6X12_BILL_AMT1X13_BILL_AMT2X14_BILL_AMT3X15_BILL_AMT4X16_BILL_AMT5X17_BILL_AMT6X18_PAY_AMT1X19_PAY_AMT2X20_PAY_AMT3X21_PAY_AMT4X22_PAY_AMT5X23_PAY_AMT6X24_default payment next month
020000female2married2422-1-1-2-239133102689000068900001
1120000female2single26-1200022682172526823272345532610100010001000020001
290000female2single340000002923914027135591433114948155491518150010001000100050000
350000female2married370000004699048233492912831428959295472000201912001100106910000
450000male2married57-10-100086175670358352094019146191312000366811000090006896790
550000male1single3700000064400570695760819394196192002425001815657100010008000
6500000male1single290000003679654120234450075426534830034739445500040000380002023913750137700
7100000female2single230-1-100-111876380601221-1595673806010581168715420
8140000female3married2800200011285140961210812211117933719332904321000100010000
920000male3single35-2-2-2-2-1-10000130071391200013007112200

Last rows

X1_LIMIT_BALX2_GENDERX3_EDUCATIONX4_MARITAL_STATUSX5_AGEX6_PAY_0X7_PAY_2X8_PAY_3X9_PAY_4X10_PAY_5X11_PAY_6X12_BILL_AMT1X13_BILL_AMT2X14_BILL_AMT3X15_BILL_AMT4X16_BILL_AMT5X17_BILL_AMT6X18_PAY_AMT1X19_PAY_AMT2X20_PAY_AMT3X21_PAY_AMT4X22_PAY_AMT5X23_PAY_AMT6X24_default payment next month
29990140000male2married4100000013832513714213911013826249675461216000700042281505200020000
29991210000male2married343222222500250025002500250025000000001
2999210000male3married43000-2-2-288021040000002000000000
29993100000male1single380-1-100030421427102996706266947355004200011178440003000200020000
2999480000male2single342222227255777708793847751982607811587000350007000040001
29995220000male3married3900000018894819281520836588004312371598085002000050033047500010000
29996150000male3single43-1-1-1-100168318283502897951900183735268998129000
2999730000male2single37432-10035653356275820878205821935700220004200200031001
2999880000male3married411-1000-1-16457837976304527741185548944859003409117819265296418041
2999950000male2married460000004792948905497643653532428153132078180014301000100010001

Duplicate rows

Most frequent

X1_LIMIT_BALX2_GENDERX3_EDUCATIONX4_MARITAL_STATUSX5_AGEX6_PAY_0X7_PAY_2X8_PAY_3X9_PAY_4X10_PAY_5X11_PAY_6X12_BILL_AMT1X13_BILL_AMT2X14_BILL_AMT3X15_BILL_AMT4X16_BILL_AMT5X17_BILL_AMT6X18_PAY_AMT1X19_PAY_AMT2X20_PAY_AMT3X21_PAY_AMT4X22_PAY_AMT5X23_PAY_AMT6X24_default payment next monthcount
020000male2single2422444416501650165016501650165000000012
150000female1single231-2-2-2-2-200000000000002
250000male2single261-2-2-2-2-200000000000002
380000female2married31-2-2-2-2-2-200000000000002
480000female2single25-2-2-2-2-2-200000000000002
580000female3married42-2-2-2-2-2-200000000000002
690000female1single311-2-2-2-2-200000000000002
7100000female2married491-2-2-2-2-200000000000002
8110000female1single311-2-2-2-2-200000000000002
9140000male1single291-2-2-2-2-200000000000002